Corresponding to a strong need and supported by an active scientific community, numerous algorithmic solutions have been proposed for the detection of marine animals.
A series of international workshops held since 2003 echoes this dynamic:                Special Issue, “Detection and localization of marine mammals using passive acoustics”, Canadian Acoustics, Vol. 32, 2004.        Special Issue, “Detection and localization of marine mammals using passive acoustics”, Applied Acoustics, vol. 67, 2006.        Special Issue, “Detection and classification of marine mammals using passive acoustics”, Canadian Acoustics, Vol. 36, 2008.        Special Issue, “Detection, classification, localization and census of marine mammals with passive acoustics monitoring”, Applied Acoustics, vol. 71, 2010.        
Through these four references, algorithmic solutions for the detection of clicks can be identified:                a) using the classic solution of the energy descriptor: W. Mr. X. Zimmer, J. Harwood, P. L. Tyack, P. Johnson, and P. T. Madsen, “Passive acoustic detection of deep-diving beaked whales”, The Journal of the Acoustical Society of America, vol. 124, pp. 2823-2832, 2008.        b) using the original solution of the Teager descriptor: V. Kandia and Y. Stylianou, “Detection of sperm whale clicks based On The Teager-Kaiser energy operator”, Applied Acoustic, Vol. 67, pp. 1144-1163, 2006.        c) using the original solution of the kurtosis descriptor: C. Gervaise, A. Barazzutti, S. Busson, Y. Simard, and N. Roy, “Automatic detection of Bioacoustics impulses based on kurtosis under weak signal to noise ratio”, Applied Acoustics, vol. 71, pp. 1020-1026, 2010.        
For the detection of whistles, algorithmic solutions have been proposed:                a) using the spectrogram: D. K. Mellinger and C. W. Clark, “Recognizing transient low-frequency whale sounds by spectrogram correlation”, The Journal of the Acoustical Society of America, Vol. 107, pp. 3518-3529, 2000.        b) using the Hilbert Huang Transform: Adam O (2006), “Advantages of the Hilbert Huang transform for marine mammals signals analysis”, J. Acoust. Soc. Am 120: 2965-2973.        c) using the ambiguity function at higher orders and warping operators: C. Ioana, C. Gervaise, Y. Stephan, and J. I. March, “Analysis of underwater mammal vocalizations using time-frequency-phase tracker”, Applied Acoustics, vol. 71, pp. 1070-1080, 2010.        
In general, at best, the above known solutions have an adaptability in frequency (allowing to select the frequency band on which exists the signal, and to reject the ambient noise in other frequency bands) and they select a specific detection test, which is compared to an estimated value of this detection test in the case of a measurement noise alone.
Unfortunately, these known solutions suffer from several limitations:                they are not embeddable in an autonomous communicating system;        their performances are fixed or depend on the presence of a trained operator to adjust the settings or the architecture of the algorithmic solutions;        they do not adapt automatically to the properties (which are often variable) of biological sound productions;        they do not treat the entire sound production of marine animals in a single process;        they do not include learning and rejections of false alarms generated by ambient noise.        